Komparasi Algoritma Data Mining Menggunakan Forward selection Pada Prediksi Harga Jagung

Lasulika, Mohamad Efendi; Bode, Andi

doi:10.51158/tecnoscienza.v5i2.392

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Data mining is the process of using statistics, mathematics, artificial intelligence, and machine learning techniques to extract or identify useful information from relevant databases for relevant knowledge [7]. The term data mining is a discipline whose main goal is to find, examine or extract knowledge from data or information available to us.…”

Section: A Data Miningmentioning

confidence: 99%

The K-Nearest Neighbor Algorithm using Forward Selection and Backward Elimination in Predicting the Student’s Satisfaction Level of University Ichsan Gorontalo toward Online Lectures during the COVID-19 Pandemic

2023

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Academic services are actions taken by state and private universities to provide convenience for student’s academic activities. During the current covid-19 pandemic, every university remains active in academic activities. This study aimed to apply the K-Nearest Neighbor algorithm in predicting the level of student satisfaction with online lectures at University Ichsan Gorontalo. Our main aim was to obtain quantitative information to measure student satisfaction with online lectures during the pandemic, which should be taken into account when making decisions. K-Nearest Neighbor is a non-parametric Algorithm that can be used for classification and regression, but K-Nearest Neighbor are better if feature selection is applied in selecting features that are not relevant to the model. Feature Selection used in this research is Forward Selection and Backward Elimination. Seeing the results of experiments that have been carried out with the application of the K-nearest Neighbor algorithm and the selection feature, the results of the forecasting can be used for consideration or policy in decision making. The highest level of accuracy in the K-Nearest Neighbor algorithm model used Forward Selection with an accuracy rate of 98.00%. Thus, the experimental results showed that feature selection, namely forward selection, was a better model in the relevant selection variables compared to backward elimination.

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Section: A Data Miningmentioning

confidence: 99%

The K-Nearest Neighbor Algorithm using Forward Selection and Backward Elimination in Predicting the Student’s Satisfaction Level of University Ichsan Gorontalo toward Online Lectures during the COVID-19 Pandemic

2023

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“…Prabowo B.U., et al (2019) combined the K-NN method with linear regression to predict gold prices, resulting in improved predictive performance [7]. M. Efendi L and Andi Bode (2021) compared feature selection methods in corn price prediction, finding that feature selection significantly reduced the RMSE values [8]. These studies highlight the importance and effectiveness of feature selection in forecasting.…”

Section: Introductionmentioning

confidence: 99%

K-Nearest Neighbor for Gorontalo City Chili Price Prediction Using Feature Selection, Backward Elimination, and Forward Selection

Labolo,

Utiarahman,

Lasulika

et al. 2023

ijsecs

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This study addresses chili price volatility, an important concern that impacts the national economy and societal welfare. Fluctuations in chili prices in the retail market greatly influence market demand, thereby influencing farming decisions, especially chili cultivation. To help make better decisions, Researchers use forecasting, which is defined as the projection of future trends based on the analysis of historical data, using statistical methods. The K-Nearest Neighbor (K-NN) algorithm is used because of its resistance to high noise on large training datasets. However, challenges arise in determining the optimal value of 'k' and selecting related attributes. To overcome this, Feature Selection is applied to refine the model by removing irrelevant features, resulting in a significant reduction in the model error rate. This improvement indicates an increase in the efficiency of the K-NN algorithm with the incorporation of Feature Selection. Our findings show that the model, with backward elimination in Feature Selection, achieves a Root Mean Square Error (RMSE) of 0.202, outperforming the model using forward selection. The prediction accuracy of this model reaches an average of 78.86%, which is much higher than the baseline data of 50%. This shows the success of the proposed method in predicting chili prices.

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Komparasi Algoritma Data Mining Menggunakan Forward selection Pada Prediksi Harga Jagung

Cited by 2 publications

References 7 publications

The K-Nearest Neighbor Algorithm using Forward Selection and Backward Elimination in Predicting the Student’s Satisfaction Level of University Ichsan Gorontalo toward Online Lectures during the COVID-19 Pandemic

The K-Nearest Neighbor Algorithm using Forward Selection and Backward Elimination in Predicting the Student’s Satisfaction Level of University Ichsan Gorontalo toward Online Lectures during the COVID-19 Pandemic

K-Nearest Neighbor for Gorontalo City Chili Price Prediction Using Feature Selection, Backward Elimination, and Forward Selection

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